| # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # TODO: Address all TODOs and remove all explanatory comments | |
| """TODO: Add a description here.""" | |
| from glob import glob | |
| import os | |
| import numpy as np | |
| from PIL import Image | |
| from tokenizers import pre_tokenizers | |
| from tokenizers.pre_tokenizers import Digits, Split, Whitespace, Sequence | |
| import datasets | |
| from itertools import chain | |
| import pandas as pd | |
| # TODO: Add BibTeX citation | |
| # Find for instance the citation on arxiv or on the dataset repo/website | |
| _CITATION = """\ | |
| @InProceedings{huggingface:dataset, | |
| title = {A great new dataset}, | |
| author={huggingface, Inc. | |
| }, | |
| year={2020} | |
| } | |
| """ | |
| # TODO: Add description of the dataset here | |
| # You can copy an official description | |
| _DESCRIPTION = """\ | |
| This new dataset is designed to solve this great NLP task and is crafted with a lot of care. | |
| """ | |
| # TODO: Add a link to an official homepage for the dataset here | |
| _HOMEPAGE = "" | |
| # TODO: Add the licence for the dataset here if you can find it | |
| _LICENSE = "" | |
| # TODO: Add link to the official dataset URLs here | |
| # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. | |
| # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) | |
| _URLS = { | |
| 'train': "http://hyperion.bbirke.de/data/ref_seg/ref_seg_ger_train.zip", | |
| 'test': "http://hyperion.bbirke.de/data/ref_seg/ref_seg_ger_test.zip", | |
| } | |
| _LABELS = [ | |
| 'publisher', 'source', 'url', 'other', 'author', 'editor', 'lpage', | |
| 'volume', 'year', 'issue', 'title', 'fpage', 'edition' | |
| ] | |
| _FEATURES = datasets.Features( | |
| { | |
| "id": datasets.Value("string"), | |
| "tokens": datasets.Sequence(datasets.Value("string")), | |
| # "attention_mask": datasets.Sequence(datasets.Value("int64")), | |
| # "bbox": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))), | |
| # "RGBs": datasets.Sequence(datasets.Sequence(datasets.Value("int64"))), | |
| # "fonts": datasets.Sequence(datasets.Value("string")), | |
| # "image": datasets.Array3D(shape=(3, 224, 224), dtype="uint8"), | |
| # "original_image": datasets.features.Image(), | |
| "labels": datasets.Sequence(datasets.features.ClassLabel( | |
| names=list(chain.from_iterable([['B-' + x, 'I-' + x] for x in _LABELS])) + ['O'] | |
| )), | |
| "labels_ref": datasets.Sequence(datasets.features.ClassLabel( | |
| names=['B-ref', 'I-ref', ] | |
| )) | |
| # These are the features of your dataset like images, labels ... | |
| } | |
| ) | |
| # def load_image(image_path, size=None): | |
| # image = Image.open(image_path).convert("RGB") | |
| # w, h = image.size | |
| # if size is not None: | |
| # # resize image | |
| # image = image.resize((size, size)) | |
| # image = np.asarray(image) | |
| # image = image[:, :, ::-1] # flip color channels from RGB to BGR | |
| # image = image.transpose(2, 0, 1) # move channels to first dimension | |
| # return image, (w, h) | |
| # def normalize_bbox(bbox, size): | |
| # return [ | |
| # int(1000 * int(bbox[0]) / size[0]), | |
| # int(1000 * int(bbox[1]) / size[1]), | |
| # int(1000 * int(bbox[2]) / size[0]), | |
| # int(1000 * int(bbox[3]) / size[1]), | |
| # ] | |
| # | |
| # | |
| # def simplify_bbox(bbox): | |
| # return [ | |
| # min(bbox[0::2]), | |
| # min(bbox[1::2]), | |
| # max(bbox[2::2]), | |
| # max(bbox[3::2]), | |
| # ] | |
| # | |
| # | |
| # def merge_bbox(bbox_list): | |
| # x0, y0, x1, y1 = list(zip(*bbox_list)) | |
| # return [min(x0), min(y0), max(x1), max(y1)] | |
| # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case | |
| class RefSeg(datasets.GeneratorBasedBuilder): | |
| """TODO: Short description of my dataset.""" | |
| CHUNK_SIZE = 256 | |
| VERSION = datasets.Version("1.0.0") | |
| # This is an example of a dataset with multiple configurations. | |
| # If you don't want/need to define several sub-sets in your dataset, | |
| # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. | |
| # If you need to make complex sub-parts in the datasets with configurable options | |
| # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig | |
| # BUILDER_CONFIG_CLASS = MyBuilderConfig | |
| # You will be able to load one or the other configurations in the following list with | |
| # data = datasets.load_dataset('my_dataset', 'first_domain') | |
| # data = datasets.load_dataset('my_dataset', 'second_domain') | |
| # BUILDER_CONFIGS = [ | |
| # datasets.BuilderConfig(name="sample", version=VERSION, | |
| # description="This part of my dataset covers a first domain"), | |
| # datasets.BuilderConfig(name="full", version=VERSION, | |
| # description="This part of my dataset covers a second domain"), | |
| # ] | |
| # DEFAULT_CONFIG_NAME = "small" # It's not mandatory to have a default configuration. Just use one if it make sense. | |
| split_tokens = [".", ":", ",", ";", "/", "-", "(", ")"] | |
| TOKENIZER = Sequence([ | |
| Whitespace(), | |
| Digits(), | |
| ] + [Split(x, behavior="isolated") for x in split_tokens]) | |
| #TOKENIZER = Whitespace() | |
| def _info(self): | |
| # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset | |
| return datasets.DatasetInfo( | |
| # This is the description that will appear on the datasets page. | |
| description=_DESCRIPTION, | |
| # This defines the different columns of the dataset and their types | |
| features=_FEATURES, # Here we define them above because they are different between the two configurations | |
| # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and | |
| # specify them. They'll be used if as_supervised=True in builder.as_dataset. | |
| # supervised_keys=("sentence", "label"), | |
| # Homepage of the dataset for documentation | |
| homepage=_HOMEPAGE, | |
| # License for the dataset if available | |
| license=_LICENSE, | |
| # Citation for the dataset | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration | |
| # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name | |
| # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS | |
| # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. | |
| # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive | |
| data_dir = dl_manager.download_and_extract(_URLS) | |
| # print(data_dir) | |
| # with open(os.path.join(data_dir, "train.csv")) as f: | |
| # files_train = [{'id': row['id'], 'filepath_txt': os.path.join(data_dir, row['filepath_txt']), | |
| # 'filepath_img': os.path.join(data_dir, row['filepath_img'])} for row in | |
| # csv.DictReader(f, skipinitialspace=True)] | |
| # with open(os.path.join(data_dir, "test.csv")) as f: | |
| # files_test = [{'id': row['id'], 'filepath_txt': os.path.join(data_dir, row['filepath_txt']), | |
| # 'filepath_img': os.path.join(data_dir, row['filepath_img'])} for row in | |
| # csv.DictReader(f, skipinitialspace=True)] | |
| # with open(os.path.join(data_dir, "validation.csv")) as f: | |
| # files_validation = [{'id': row['id'], 'filepath_txt': os.path.join(data_dir, row['filepath_txt']), | |
| # 'filepath_img': os.path.join(data_dir, row['filepath_img'])} for row in | |
| # csv.DictReader(f, skipinitialspace=True)] | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": data_dir['train'], | |
| "split": "train", | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": data_dir['test'], | |
| "split": "test" | |
| }, | |
| ), | |
| ] | |
| # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
| def _generate_examples(self, filepath, split): | |
| # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. | |
| # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. | |
| # print(filepath) | |
| # print(split) | |
| paths = glob(filepath + '/' + split + '/*.csv') | |
| key = 0 | |
| for f in paths: | |
| df = pd.read_csv(f, keep_default_na=False) | |
| input_ids = [] | |
| labels = [] | |
| refs = [] | |
| for i, row in df.iterrows(): | |
| # tokenized_input = row['token'].split(' ') | |
| tkn = self.TOKENIZER.pre_tokenize_str(row['token']) | |
| if row['label'] == 'identifier': | |
| row['label'] = 'other' | |
| if not tkn: | |
| continue | |
| tokenized_input, offsets = zip(*tkn) | |
| tokenized_input = list(tokenized_input) | |
| for t in range(len(tokenized_input)): | |
| if t == 0: | |
| refs.append(row['ref'] + '-ref') | |
| else: | |
| refs.append('I-ref') | |
| if len(tokenized_input) > 1: | |
| if row['tag'] == 'B': | |
| if tokenized_input[0] == '': | |
| continue | |
| input_ids.append(tokenized_input[0]) | |
| labels.append(row['tag'] + '-' + row['label']) | |
| for input_id in tokenized_input[1:]: | |
| if input_id == '': | |
| continue | |
| input_ids.append(input_id) | |
| labels.append('I-' + row['label']) | |
| elif row['tag'] == 'I': | |
| for input_id in tokenized_input: | |
| input_ids.append(input_id) | |
| labels.append('I-' + row['label']) | |
| else: | |
| if tokenized_input[0] == '': | |
| continue | |
| for input_id in tokenized_input: | |
| input_ids.append(input_id) | |
| labels.append('O') | |
| elif len(tokenized_input) == 1: | |
| if tokenized_input[0] == '': | |
| continue | |
| input_ids.append(tokenized_input[0]) | |
| if row['tag'] == 'O': | |
| labels.append(row['tag']) | |
| else: | |
| labels.append(row['tag'] + '-' + row['label']) | |
| else: | |
| continue | |
| clean_input_ids = [] | |
| clean_labels = [] | |
| clean_refs = [] | |
| for i, input in enumerate(input_ids): | |
| if input != '': | |
| clean_input_ids.append(input) | |
| clean_labels.append(labels[i]) | |
| clean_refs.append(refs[i]) | |
| # n_chunks = int(len(clean_input_ids) / self.CHUNK_SIZE) if len(clean_input_ids) % self.CHUNK_SIZE == 0 \ | |
| # else int(len(clean_input_ids) / self.CHUNK_SIZE) + 1 | |
| # split_ids = np.array_split(clean_input_ids, n_chunks) | |
| # split_labels = np.array_split(clean_labels, n_chunks) | |
| # split_refs = np.array_split(clean_refs, n_chunks) | |
| # print(clean_input_ids) | |
| # for chunk_ids, chunk_labels, chunk_refs in zip(clean_input_ids, clean_labels, clean_refs): | |
| # for chunk_id, index in enumerate(range(0, len(clean_input_ids), self.CHUNK_SIZE)): | |
| # split_ids = clean_input_ids[index:max(len(clean_input_ids), index + self.CHUNK_SIZE)] | |
| # split_bboxes = bboxes[index:index + self.CHUNK_SIZE] | |
| # split_rgbs = rgbs[index:index + self.CHUNK_SIZE] | |
| # split_fonts = fonts[index:index + self.CHUNK_SIZE] | |
| # split_labels = clean_labels[index:max(len(clean_input_ids), index + self.CHUNK_SIZE)] | |
| # split_labels_post = [item for sublist in split_labels for item in sublist] | |
| # if(len(split_ids) != len(split_labels)): | |
| # print(f) | |
| # print(len(input_ids), input_ids) | |
| # print(len(split_labels), split_labels) | |
| # for s in split_labels: | |
| # if type(s) is not str: | |
| # print(f) | |
| # print(len(input_ids), input_ids) | |
| # print(len(split_labels), split_labels) | |
| # print(len(split_labels_post), split_labels_post) | |
| # print(split_labels, len(split_labels)) | |
| # print(split_ids, len(split_ids)) | |
| yield key, { | |
| "id": f"{os.path.basename(f)}", | |
| 'tokens': clean_input_ids, | |
| # 'attention_mask': [1] * len(chunk_ids), | |
| # "bbox": split_bboxes, | |
| # "RGBs": split_rgbs, | |
| # "fonts": split_fonts, | |
| # "image": image, | |
| # "original_image": original_image, | |
| "labels": clean_labels, | |
| "labels_ref": clean_refs | |
| } | |
| key += 1 | |